Construction and validation of a mortality prediction model for patients with sepsis-associated encephalopathy: Interpretable machine learning approach.

ABSTRACT:

Objectives

Sepsis-associated encephalopathy (SAE) is a prevalent complication among critically ill sepsis patients with poor outcomes. This study aimed to develop and validate an interpretable machine learning model for predicting mortality in SAE patients to support clinical decision-making.

Methods

The study utilized two large critical care databases: the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database for model construction and internal validation, and the eICU Collaborative Research Database (eICU-CRD, version 2.0.1) for external validation. The XGBoost model was trained to predict patient mortality at 28 and 90 days after ICU admission, and performance was assessed by indicators including the area under the curve (AUC). To improve the int

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12576211 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature